HARVEST-AI: A UAV-Based Artificial Intelligence System for Crop Health Monitoring

Cutting-edge technologies to find diseases in plants are important for farming. Crop diseases threaten the world's food security. Thus, quick and precise disease diagnosis minimizes output losses and promotes sustainable farming methods. This work presents a new idea on how agricultural disease diagnosis can be transformed using IoT and ML with UAVs. Hubs with strategically placed Internet of Things (IoT) sensors capture the real-time picture of the environmental conditions prevailing in farms and high-definition pictures of the crops are taken using cameras installed in the farm and through drones. This data is used by machine learning (ML) models, namely Convolutional Neural Networks (CNNs), to reliably and early identify illnesses. We can improve decisions by associating environmental parameters with outbreaks of diseases by integrating IoT capabilities. The outcomes demonstrate promise for lowering the requirement for chemical treatments and continuously displaying excellent accuracy in illness identification, implementation achieved a reduction in chemical pesticide use by 40%, and an increase in crop yields by 25% coupled with a decrease in crop diseases by 30%. This research makes a very impressive contribution to the efforts of advocating for sustainable farming practices and the global fight against food insecurity. The study concludes with a range of comprehensive usability assessments, including exploratory evaluations. Additionally, it presents reflections on potential future developments and acknowledges current limitations in the sector of agriculture.